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	<title>slime mould algorithm &#8211; BIOENGINEER.ORG</title>
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		<title>Slime Mould Meets Graph AI to Hunt Hidden Faults in Smart Substations</title>
		<link>https://bioengineer.org/slime-mould-meets-graph-ai-to-hunt-hidden-faults-in-smart-substations/</link>
		
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		<pubDate>Sun, 11 Oct 2026 19:20:56 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Attention Mechanism]]></category>
		<category><![CDATA[fault localization]]></category>
		<category><![CDATA[graph autoencoder]]></category>
		<category><![CDATA[graph convolutional network]]></category>
		<category><![CDATA[graph neural networks]]></category>
		<category><![CDATA[hyperparameter optimization]]></category>
		<category><![CDATA[IEC 61850]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[power grid]]></category>
		<category><![CDATA[secondary virtual circuits]]></category>
		<category><![CDATA[slime mould algorithm]]></category>
		<category><![CDATA[smart substation]]></category>
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					<description><![CDATA[Researchers combined a graph autoencoder, an attention-equipped graph convolutional network, and a slime-mould-inspired optimizer to locate hidden faults in smart substation secondary virtual circuits with 98.05 percent accuracy.]]></description>
		
		
		
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